yes,They are in the correct format for analysis. ## 3. Format the data
# format the datecv_states$date <-as.Date(cv_states$date, format="%Y-%m-%d")# format the state and state abbreviation (abb) variablesstate_list <-unique(cv_states$state)cv_states$state <-factor(cv_states$state, levels = state_list)abb_list <-unique(cv_states$abb)cv_states$abb <-factor(cv_states$abb, levels = abb_list)### FINISH THE CODE HERE # order the data first by state, second by datecv_states = cv_states[order(cv_states$state, cv_states$date),]# Confirm the variables are now correctly formattedstr(cv_states)
## yes,correctly formated# Inspect the range values for each variable. What is the date range? The range of cases and deaths?head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"
Date range:from 2020-01-21 to 2023-03-23
The range of cases and deaths:varies from 1 to 12,169,158 //// the deaths range from 0 to 104,277.
4. Add new_cases and new_deaths and correct outliers.
# Add variables for new_cases and new_deaths:for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),]# add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1]### FINISH THE CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1] }# include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths}# Focus on recent datescv_states <- cv_states %>% dplyr::filter(date >="2021-06-01")### FINISH THE CODE HERE# Inspect outliers in new_cases using plotlyp1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5)ggplotly(p1)
p1<-NULL# to clear from workspacep2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5)ggplotly(p2)
p2<-NULL# to clear from workspace# set negative new case or death counts to 0cv_states$new_cases[cv_states$new_cases<0] =0cv_states$new_deaths[cv_states$new_deaths<0] =0# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i])# add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1]### FINISH CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1] }# include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths}# Smooth new countscv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>%round(digits =0)cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>%round(digits =0)# Inspect data again interactivelyp2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5)ggplotly(p2)
#p2=NULL
5. Add additional variables
### FINISH CODE HERE# add population normalized (by 100,000) counts for each variablecv_states$per100k =as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))cv_states$newper100k =as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / casescv_states = cv_states %>%mutate(naive_CFR =round((deaths*100/cases),2))# create a `cv_states_today` variablecv_states_today =subset(cv_states, date==max(cv_states$date))
II. Scatterplots
6. Explore scatterplots using plot_ly()
### FINISH CODE HERE# pop_density vs. casescv_states_today %>%plot_ly(x =~pop_density, y =~cases,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# filter out "District of Columbia"cv_states_today_filter <- cv_states_today %>%filter(state!="District of Columbia")# pop_density vs. cases after filteringcv_states_today_filter %>%plot_ly(x =~pop_density, y =~cases, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# pop_density vs. deathsper100kcv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# Adding hoverinfocv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5),hoverinfo ='text',text =~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ", deathsper100k, sep=""), sep ="<br>")) |>layout(title ="Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",yaxis =list(title ="Deaths per 100k"), xaxis =list(title ="Population Density"),hovermode ="compare")
Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
#Do you think pop_density is a correlate of newdeathsper100k?
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).
Warning: The following aesthetics were dropped during statistical transformation: size.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
#Might be somewhat related to deaths per 100k, it does not seem to be a strong or direct correlation.
8. Multiple line chart
#How have they changed over time? #What is the time delay between the peak of cases and the peak of deaths?plot_ly(cv_states, x =~date, y =~naive_CFR, color =~state, type ="scatter", mode ="lines")
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
### FINISH CODE HERE# Line chart for Florida showing new_cases and new_deaths togethercv_states %>%filter(state=="Florida") %>%plot_ly(x =~date, y =~new_cases, type ="scatter", mode ="lines") %>%add_lines(x =~date, y =~new_deaths, type ="scatter", mode ="lines")
# In some states,naive_CFR and deaths per new case begins to rise in September but then shows an overall decline from January 2022.# The peak in new cases occurs on January 10, 2022, the peak in deaths happened earlier, around September 20, 2021.
9. Heatmaps
#Use plot_ly() to create a heatmap out of this matrix. Which states stand out?#Repeat with newper100k variable. Now which states stand out?### FINISH CODE HERE# Map state, date, and new_cases to a matrixlibrary(tidyr)cv_states_mat <- cv_states %>%select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
# Create a second heatmap after filtering to only include dates every other weekfilter_dates <-seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")cv_states_mat <- cv_states %>%select(state, date, newper100k) %>%filter(date %in% filter_dates)cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
# In the heat map showing new cases, California is the most prominent state in January 2022, followed by Texas, New York, and Florida,# For new cases per 100k, most states exhibit similar rates, peaking in January 2022, with a smaller increase seen around June 2022.# In a 2week heat map of new cases per 100k during the latter half of 2021, Alaska stands out in October, Louisiana and Mississippi stand out in August.
10. Map
#Describe the difference in the pattern of the CFR.### For specified datepick.date ="2021-10-15"# Extract the data for each state by its abbreviationcv_per100 <- cv_states %>%filter(date==pick.date) %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Make sure both maps are on the same color scaleshadeLimit <-125# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", pick.date), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),geo = set_map_details )fig_pick.date <- fig################ Map for today's date# Extract the data for each state by its abbreviationcv_per100 <- cv_states_today %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", Sys.Date()), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),geo = set_map_details )fig_Today <- fig### Plot together subplot(fig_pick.date, fig_Today, nrows =2, margin = .05)
# In October 2021, CFR was highest in Alaska, followed by Montana and neighboring states, while other states showed lower CFR rates.Currently, CFR is very low across all states,demonstrating significant advancements in COVID-19 treatment and prevention, supported by the broad distribution of vaccines.